Based on the given task, we need to analyze how the feature "job" relates to the target variable "Does this person receive a credit?". 

Here is the analysis of the relationship between the feature and target variable:

- Skilled job: It is likely that individuals with skilled jobs are more likely to receive credit. It can be assumed that having a steady and higher income from skilled jobs increases the chances of credit approval.
- Unskilled resident job: Individuals with unskilled resident jobs might have a lower likelihood of receiving credit. Unskilled jobs usually have lower income and stability, which can make credit approval less likely.
- High qualif/self emp/mgmt job: This category includes high qualification, self-employed, and management jobs. People in these professions usually have higher incomes and stability, which can increase the likelihood of receiving credit.
- Unemp/unskilled non res job: Being unemployed or having an unskilled non-residential job can negatively impact the chances of receiving credit. These categories indicate lower income and stability, which are factors that credit lenders often consider.

Based on this analysis, the dictionary would look like:

```json
{
	"yes": ["skilled", "high qualif/self emp/mgmt"],
	"no": ["unskilled resident", "unemp/unskilled non res"]
}
```